• Journal of the Korean Society for Nondestructive Testing
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• v.28 no.3
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• pp.254-262
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• 2008

The objective of the proposed study is to produce a tool-condition monitoring (TCM) strategy that will lead to a more efficient and economical drilling tool usage. Drill-wear monitoring is an important attribute in the automatic cutting processes as it can help preventing damages of the tools and workpieces and optimizing the tool usage. This study presents the architectures of a multi-layer feed-forward neural network with back-propagation training algorithm for the monitoring of drill wear. The input features to the neural networks were extracted from the AE signals using the wavelet transform analysis. Training and testing were performed under a moderate range of cutting conditions in the dry drilling of steel plates. The results indicated that the extracted input features from AE signals to the supervised neural networks were effective for drill wear monitoring and the output of the neural networks could be utilized for the tool life management planning.

• Journal of the Korea Concrete Institute
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• v.12 no.3
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• pp.115-123
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• 2000

Characterizing the electromagnetic properties of concrete is essential to the enhancement of accuracy and reliability in nondestructive testing of concrete structures using electromagnetic techniques. To establish a data base for the properties of concrete, a measurement technique has been developed and a set of data has been obtained for the frequency range of 1~6 GHz. As moisture content is one of major contributing factors to determine permittivity of dielectric material, moisture content is varied during the measurement. An application of a measurement system which consists of open-ended coaxial probe and automatic network analyzer to concrete and mortar specimens is studied. For this, calibration techniques, size of specimens, and number of measurements necessary to obtain reliable data are investigated. From the measured data, it is shown that moisture content plays an important role to determine the permittivity of specimens. As the moisture content increases. The permittivity of specimens show tendency to approach the permittivity of water.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.492-496
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• 1987

The testing philosophy and control schemes are investigated and applied to construct the Automatic Microwave Leakage Inspection System (AMLIS) . AMLIS is consists of three major parts such as Material Handling Mechanism, Fine Positioning Mechanism and Scanning Mechanism. The material Handling unit is designed to perform loading and unloading microwave oven onto the testing point by pneumatic cylinder and vacuum pump. The Fine positioning part includes X-Y-.THETA. table and distance sensing equipment. The scanning part is composed of five SCARA robots, which traverse X-Y-Z catesian coordinates respectively. The leakage testing probes are placed at the end of this each robot then the path and speed are both controlled via microprocessors. A performance test of this system combined with electric parts and software is done and the basic major function of system are accomplished.

• Journal of the Korean Society for Nondestructive Testing
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• v.31 no.6
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• pp.626-634
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• 2011

The investigation of concrete structure typically relies on visual inspection which is one of the basic inspection techniques. Image processing techniques play a crucial role in the growing field of automatic surface inspection technique. However, kinds of inspection equipment, environmental condition and detection algorithm have much influence on the reliability of inspection result. This paper proposes a verification method and testing procedure for the reliability of inspection results and surveys characteristics of image acquisition systems and crack inspection algorithms.

• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.2
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• pp.1-8
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• 2011

In this study, software products developed in the course of testing, software managers in the process of testing software test and test tools for effective learning effects perspective has been studied using the NHPP software. The finite failure non-homogeneous Poisson process models presented and the life distribution applied extreme distribution which used to find the minimum (or the maximum) of a number of samples of various distributions. Software error detection techniques known in advance, but influencing factors for considering the errors found automatically and learning factors, by prior experience, to find precisely the error factor setting up the testing manager are presented comparing the problem. As a result, the learning factor is greater than automatic error that is generally efficient model could be confirmed. This paper, a numerical example of applying using time between failures and parameter estimation using maximum likelihood estimation method, after the efficiency of the data through trend analysis model selection were efficient using the mean square error.

• Proceedings of the IEEK Conference
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• 2002.06a
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• pp.235-238
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• 2002

In this paper we describe the design and implementation of Electrical Ground Support Equipment(EGSE) for the CBS transponder testing. The main task of EGSE is to check out satellite systems, at system or subsystem level, during integration and validation phases of their life-cycle. Through a combination of hardware and software elements, EGSE supports manual, semi-automatic and fully automated testing. Automation is achieved by offering users simple, yet powerful means to write their own test application programs (test sequences) in high-level, test-oriented language and to run them in a strict real-time environment. The core of this environment is a user-configurable real-time database, containing all the information needed to calibrate acquired data, check them against predefined thresholds, automatically react to out-of-range conditions, display data using animated graphics or synoptic windows, and so on.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.6
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• pp.127-134
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• 2004

As the result of reduction of the barrier of national economy on matters of quality assurance of weighing instruments in recently, it is considering in domestic as well as international matters. Therefore, this study is to analysis and compare with international md national measuring standards on the electric self-indicating scale, to improve the quality of electronic machine by providing a reformation plan on currently problems of domestic manufactured, and to identify to introduce in domestic criteria from international standards. I had compared KSC 1313 to OIML R 76-1 as the performance assessment items, such as metrology Properties test, static temperature test, temperature effect testing under the un-load, electronic wave disturbance test, high-tempereature and high-humidity stability test and durability test. Therefore, only one company is passed all items of the test according to the international (reference) standards, but it is possible to improve the quality in general if it should be use the load cell and electric components which is stability of temperature change in order to supplementation to the static temperature test and temperature effect testing under the un-load. It is also possible to apply in the domestic with the OIML 76-1 after correcting the design.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.404-420
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• 2021

In this paper, an automatic system of locating the indoor area with weak or no mobile signal was proposed and demonstrated experimentally by using the Internet of Things (IoT) technology. Nowadays, the technicians of mobile services providers need to go along with numerous heavy equipment to measure and record the mobile signal strength at outside environment. Recently, there are systems proposed to do such measurement at outdoor area by using the IoT technology automatically. However, these works could not be applied in the indoor area since there are difficulties to do the indoor mapping and positioning. In this work, the Bluetooth Low Energy (BLE) was used to tackle these two difficulties. After a proper placement of BLE in the testing site, while the technician walk around with a handheld analyzer, the data can be obtained accordingly for further analysis in the proposed system which includes the construction of floor plan, detection of mobile signal strength and suggestion of indoor base stations. The gift wrapping and centroid algorithms were used during the analysis. The experimental results showed that the proposed system successfully demonstrated the indoor mapping, positioning of weak mobile signal area and suggestion of indoor base stations for the normal rectangular rooms with an area of 100 m² on single floor.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.51-62
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• 2021

Plant disease is one of the issues that can create losses in the production and economy of the agricultural sector. Early detection of this disease for finding solutions and treatments is still a challenge in the sustainable agriculture field. Currently, image processing techniques and machine learning methods have been applied to detect plant diseases successfully. However, the effectiveness of these methods still needs to be improved, especially in multiclass plant diseases classification. In this paper, a convolutional neural network with a batch normalization-based deep learning approach for classifying plant diseases is used to develop an automatic diagnostic assistance system for leaf diseases. The significance of using deep learning technology is to make the system be end-to-end, automatic, accurate, less expensive, and more convenient to detect plant diseases from their leaves. For evaluating the proposed model, an experiment is conducted on a public dataset contains 20654 images with 15 plant diseases. The experimental validation results on 20% of the dataset showed that the model is able to classify the 15 plant diseases labels with 96.4% testing accuracy and 0.168 testing loss. These results confirmed the applicability and effectiveness of the proposed model for the plant disease detection task.

• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.6
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• pp.504-509
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• 2016

Defects in dissimilar metal welds are reported to be on the increase during the operating lifespan and aging of nuclear power plants. In Korea, reported cases of defects due to dissimilar metal welds include the drain nozzle of a steam generator and RCS hot tube sampling nozzles. Therefore, there is an urgent need to develop a reliable automated nondestructive inspection technique and a system for the inspection of dissimilar metal welds of small diameter nozzles in a high radiation area of a nuclear power plant. In this study, to ensure effective defect inspection of small diameter nozzles (RCS high-temperature tube sampling nozzle) of a nuclear power plant, three different methods were developed. These include: (1) optimum inspection probe design by beam simulation, (2) multi-directions UT optimum inspection technique for the inspection of small diameters of different welded parts, and (3) remote control automatic inspection system. The developed technique and systems have been verified to be suitable for use in the inspection of defects in smaller diameter nozzles in nuclear power plants.